* Corresponding Author
Hide
Show all
Enhancing Neural Collaborative Filtering with Metadata for Book Recommender System
https://doi.org/10.22146/ijccs.103611 Putri Ayu Sedyo Mukti(1), Z. K. A. Baizal(2*)
(1) Telkom University
(2) Telkom University
(*) Corresponding Author
Abstract
Book recommender systems often face the challenges of information overload and item cold start due to the dynamics of the evolving book market. This paper proposes Feature Enhanced Neural Collaborative Filtering (FENCF), which is a novel method that combines the interaction between users and items with genre metadata information to address the item cold start problem and improve the accuracy of rating predictions. The uniqueness of FENCF lies in the preprocessing of metadata genres, which is different from typical book recommendation research. Experiments with the Amazon book dataset show the contribution of FENCF, which outperforms NCF by reducing RMSE by 4.04% and MAE by 2.73%. In addition, FENCF is also better able to cope with item cold start, with lower MAE across all testing data scenarios. The advantages of FENCF in improving rating accuracy and overcoming item cold start on complex data are very relevant to the actual condition of book sales in e-commerce, which is dynamic. In real-world applications, FENCF can accurately recommend old and new books according to each user's preference. This not only encourages users to stay with the e-commerce platform in the long run but also has the potential to increase the conversion rate of sales.
Keywords
Full Text:
PDFReferences
[1] Yang, X., & Shi, Y. (2020). Self-attention-based group recommendation. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). https://doi.org/10.1109/itnec48623.2020.9085011
[2] Nahta, R., Meena, Y. K., Gopalani, D., & Chauhan, G. S. (2021). Embedding metadata using deep collaborative filtering to address the cold start problem for the rating prediction task. Multimedia Tools and Applications, 80(12). https://doi.org/10.1007/s11042-021-10529-4
[3] Jena, K. K., Bhoi, S. K., Mallick, C., Jena, S. R., Kumar, R., Long, H. V., & Son, N. T. K. (2022). Neural model based collaborative filtering for movie recommender system. International Journal of Information Technology (Singapore), 14(4). https://doi.org/10.1007/s41870-022-00858-4
[4] Margaris, D., Vassilakis, C., & Spiliotopoulos, D. (2020). What makes a review a reliable rating in recommender systems? Information Processing and Management, 57(6). https://doi.org/10.1016/j.ipm.2020.102304
[5] Kharroubi, S., Dahmani, Y., & Nouali, O. (2022). Improving collaborative recommendation based on item weight link prediction. Turkish Journal of Electrical Engineering and Computer Sciences, 30(1). https://doi.org/10.3906/elk-2008-26
[6] Gao, C., Yao, Q., Jin, D., & Li, Y. (2021). Efficient Data-specific Model Search for Collaborative Filtering. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3447548.3467399
[7] Addanki, M., Saraswathi, S., SLAVAKKAM, D. B., Challagundla, R. B., & Pamula, R. (2023). Integrating Sentiment Analysis in Book Recommender System by using Rating Prediction and DBSCAN Algorithm with Hybrid Filtering Technique.
[8] Karabila, I., Darraz, N., El-Ansari, A., Alami, N., & El Mallahi, M. (2023). Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis. Future Internet, 15(7). https://doi.org/10.3390/fi15070235
[9] Panda, D. K., & Ray, S. (2022). Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review. Journal of Intelligent Information Systems, 59(2). https://doi.org/10.1007/s10844-022-00698-5
[10] “Amazon Books Reviews,” www.kaggle.com. https://www.kaggle.com/datasets/mohamedbakhet/amazon-books-reviews
[11] Koren, Y., & Bell, R. (2021). Advances in Collaborative Filtering - Recommender Systems Handbook. Recommender Systems Handbook.
[12] Lee, H. C., Kim, Y. S., & Kim, S. W. (2024). Real-Time Movie Recommendation: Integrating Persona-Based User Modeling with NMF and Deep Neural Networks. Applied Sciences (Switzerland), 14(3). https://doi.org/10.3390/app14031014
[13] Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2024). Self-Supervised Learning for Recommender Systems: A Survey. IEEE Transactions on Knowledge and Data Engineering, 36(1). https://doi.org/10.1109/TKDE.2023.3282907
[14] Liang, W., Fan, Z., Liang, Y., & Jia, J. (2023). Cross-Attribute Matrix Factorization Model with Shared User Embedding. arXiv preprint arXiv:2308.07284.
DOI: https://doi.org/10.22146/ijccs.103611
Article Metrics
Abstract views : 582
| 
views : 262
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1